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Huang Y, Xie Y, Zhong C, Zhou F. Finding branched pathways in metabolic network via atom group tracking. PLoS Comput Biol 2021; 17:e1008676. [PMID: 33529200 PMCID: PMC7880430 DOI: 10.1371/journal.pcbi.1008676] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 02/12/2021] [Accepted: 01/05/2021] [Indexed: 12/27/2022] Open
Abstract
Finding non-standard or new metabolic pathways has important applications in metabolic engineering, synthetic biology and the analysis and reconstruction of metabolic networks. Branched metabolic pathways dominate in metabolic networks and depict a more comprehensive picture of metabolism compared to linear pathways. Although progress has been developed to find branched metabolic pathways, few efforts have been made in identifying branched metabolic pathways via atom group tracking. In this paper, we present a pathfinding method called BPFinder for finding branched metabolic pathways by atom group tracking, which aims to guide the synthetic design of metabolic pathways. BPFinder enumerates linear metabolic pathways by tracking the movements of atom groups in metabolic network and merges the linear atom group conserving pathways into branched pathways. Two merging rules based on the structure of conserved atom groups are proposed to accurately merge the branched compounds of linear pathways to identify branched pathways. Furthermore, the integrated information of compound similarity, thermodynamic feasibility and conserved atom groups is also used to rank the pathfinding results for feasible branched pathways. Experimental results show that BPFinder is more capable of recovering known branched metabolic pathways as compared to other existing methods, and is able to return biologically relevant branched pathways and discover alternative branched pathways of biochemical interest. The online server of BPFinder is available at http://114.215.129.245:8080/atomic/. The program, source code and data can be downloaded from https://github.com/hyr0771/BPFinder. Computational search of branched metabolic pathways is a fundamental problem in metabolic engineering and metabolic network analysis, which provides a systematic way of understanding the metabolism and discovering alternative pathways for synthesis of useful biomolecules. We propose BPFinder, a novel computational approach to identify branched metabolic pathways via atom group tracking. Different from other pathfinding methods using atom tracking, BPFinder tracks the movement of atom groups in metabolic network to find linear atom group conserving pathways, and merge the found linear pathways by the selected branched compounds to generate branched pathways. Based on the structure of conserved atom groups in branched compounds, we design two merging rules for branched compounds: overlapping rule and non-overlapping rule. The user can flexibly adopt these rules to accurately find the branched pathways that contain overlapping/non-overlapping conserved atom groups. BPFinder also enables the user to combine the information of compound similarity, Gibbs free energy of reactions, and conserved atom groups to sort resulting pathways. Compared with other existing methods, BPFinder can more accurately recover the known branched pathways. The alternative branched pathways returned by BPFinder reveal that the user can flexibly utilize our proposed merging rules to discover biochemically meaningful pathways of interest.
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Affiliation(s)
- Yiran Huang
- School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China
- * E-mail:
| | - Yusi Xie
- School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China
| | - Cheng Zhong
- School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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2
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Motwalli O, Uludag M, Mijakovic I, Alazmi M, Bajic VB, Gojobori T, Gao X, Essack M. PATH cre8: A Tool That Facilitates the Searching for Heterologous Biosynthetic Routes. ACS Synth Biol 2020; 9:3217-3227. [PMID: 33198455 DOI: 10.1021/acssynbio.0c00058] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Developing computational tools that can facilitate the rational design of cell factories producing desired products at increased yields is challenging, as the tool needs to take into account that the preferred host organism usually has compounds that are consumed by competing reactions that reduce the yield of the desired product. On the other hand, the preferred host organisms may not have the native metabolic reactions needed to produce the compound of interest; thus, the computational tool needs to identify the metabolic reactions that will most efficiently produce the desired product. In this regard, we developed the generic tool PATHcre8 to facilitate an optimized search for heterologous biosynthetic pathway routes. PATHcre8 finds and ranks biosynthesis routes in a large number of organisms, including Cyanobacteria. The tool ranks the pathways based on feature scores that reflect reaction thermodynamics, the potentially toxic products in the pathway (compound toxicity), intermediate products in the pathway consumed by competing reactions (product consumption), and host-specific information such as enzyme copy number. A comparison with several other similar tools shows that PATHcre8 is more efficient in ranking functional pathways. To illustrate the effectiveness of PATHcre8, we further provide case studies focused on isoprene production and the biodegradation of cocaine. PATHcre8 is free for academic and nonprofit users and can be accessed at https://www.cbrc.kaust.edu.sa/pathcre8/.
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Affiliation(s)
- Olaa Motwalli
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Saudi Electronic University (SEU), College of Computing and Informatics, Madinah 41538-53307, Kingdom of Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ivan Mijakovic
- Chalmers University of Technology, Division of Systems & Synthetic Biology, Department of Biology and Biological Engineering, Kemivägen 10, 41296 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Meshari Alazmi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, P.O. Box 2440, Ha’il 81411, Kingdom of Saudi Arabia
| | - Vladimir B. Bajic
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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3
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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4
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Gerard MF, Comelli RN. PhDSeeker: Pheromone-Directed Seeker for metabolic pathways. Biosystems 2020; 198:104259. [PMID: 32976925 DOI: 10.1016/j.biosystems.2020.104259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 07/24/2020] [Accepted: 09/17/2020] [Indexed: 11/17/2022]
Abstract
Manually finding relationship networks among compounds can be a hard and time-consuming task. However, this process is fundamental when looking for a metabolic pathway that explains how multiple compounds are related, to identify relevant pathways in organisms, filling gaps on metabolic networks, or when new mechanisms for the synthesis of important compounds are sought. Here, we present PhDSeeker, a new tool for the automatic search of metabolic pathways. This tool is able to relate simultaneously several compounds. Furthermore, its flexibility allows it to be easily configured for addressing a wide range of situations. Solutions found are provided not only in plain text but also as interactive representations that can be analyzed in a web browser. Source code is available at https://github.com/sinc-lab/phdseeker. A web service is also available at https://sinc.unl.edu.ar/web-demo/phds/. Several fully documented study cases, including their settings and solutions files, are also provided as Supplementary Material.
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Affiliation(s)
- Matias F Gerard
- Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.
| | - Raúl N Comelli
- Departamento de Medio Ambiente, Fac. de Ingeniería y Ciencias Hídricas (FICH), Univ. Nacional del Litoral (UNL), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Ciudad Universitaria CC 242 Paraje El Pozo, 3000, Santa Fe, Argentina.
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5
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Riaz MR, Preston GM, Mithani A. MAPPS: A Web-Based Tool for Metabolic Pathway Prediction and Network Analysis in the Postgenomic Era. ACS Synth Biol 2020; 9:1069-1082. [PMID: 32347714 DOI: 10.1021/acssynbio.9b00397] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Comparative and evolutionary analyses of metabolic networks have a wide range of applications, ranging from research into metabolic evolution through to practical applications in drug development, synthetic biology, and biodegradation. We present MAPPS: Metabolic network Analysis and Pathway Prediction Server (https://mapps.lums.edu.pk), a web-based tool to study functions and evolution of metabolic networks using traditional and 'omics data sets. MAPPS provides diverse functionalities including an interactive interface, graphical visualization of results, pathway prediction and network comparison, identification of potential drug targets, in silico metabolic engineering, host-microbe interactions, and ancestral network building. Importantly, MAPPS also allows users to upload custom data, thus enabling metabolic analyses on draft and custom genomes, and has an 'omics pipeline to filter pathway results, making it relevant in today's postgenomic era.
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Affiliation(s)
- Muhammad Rizwan Riaz
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore 54792, Pakistan
| | - Gail M. Preston
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, U.K
| | - Aziz Mithani
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore 54792, Pakistan
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6
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Kim SM, Peña MI, Moll M, Bennett GN, Kavraki LE. Improving the organization and interactivity of metabolic pathfinding with precomputed pathways. BMC Bioinformatics 2020; 21:13. [PMID: 31924164 PMCID: PMC6954563 DOI: 10.1186/s12859-019-3328-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 12/18/2019] [Indexed: 11/11/2022] Open
Abstract
Background The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways. Results A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest. Conclusions This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.
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Affiliation(s)
- Sarah M Kim
- Department of Computer Science, Rice University, Houston, Texas, USA
| | - Matthew I Peña
- Department of BioSciences, Rice University, Houston, Texas, USA
| | - Mark Moll
- Department of Computer Science, Rice University, Houston, Texas, USA.
| | | | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, Texas, USA
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7
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Lin GM, Warden-Rothman R, Voigt CA. Retrosynthetic design of metabolic pathways to chemicals not found in nature. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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8
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Jeffryes JG, Seaver SMD, Faria JP, Henry CS. A pathway for every product? Tools to discover and design plant metabolism. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 273:61-70. [PMID: 29907310 DOI: 10.1016/j.plantsci.2018.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/13/2018] [Accepted: 03/19/2018] [Indexed: 06/08/2023]
Abstract
The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.
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Affiliation(s)
- James G Jeffryes
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - Samuel M D Seaver
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - José P Faria
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - Christopher S Henry
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States.
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9
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Enumerating all possible biosynthetic pathways in metabolic networks. Sci Rep 2018; 8:9932. [PMID: 29967471 PMCID: PMC6028704 DOI: 10.1038/s41598-018-28007-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 06/14/2018] [Indexed: 12/24/2022] Open
Abstract
Exhaustive identification of all possible alternate pathways that exist in metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities. We developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular size between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways of a specified size producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from identifying amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying large graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein. MetQuest is available as a Python package, and the source codes can be found at https://github.com/RamanLab/metquest.
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10
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Garcia-Ruiz E, HamediRad M, Zhao H. Pathway Design, Engineering, and Optimization. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2018; 162:77-116. [PMID: 27629378 DOI: 10.1007/10_2016_12] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.
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Affiliation(s)
- Eva Garcia-Ruiz
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Mohammad HamediRad
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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11
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Kumar A, Wang L, Ng CY, Maranas CD. Pathway design using de novo steps through uncharted biochemical spaces. Nat Commun 2018; 9:184. [PMID: 29330441 PMCID: PMC5766603 DOI: 10.1038/s41467-017-02362-x] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 11/21/2017] [Indexed: 12/31/2022] Open
Abstract
Existing retrosynthesis tools generally traverse production routes from a source to a sink metabolite using known enzymes or de novo steps. Generally, important considerations such as blending known transformations with putative steps, complexity of pathway topology, mass conservation, cofactor balance, thermodynamic feasibility, microbial chassis selection, and cost are largely dealt with in a posteriori fashion. The computational procedure we present here designs bioconversion routes while simultaneously considering any combination of the aforementioned design criteria. First, we track and codify as rules all reaction centers using a prime factorization-based encoding technique (rePrime). Reaction rules and known biotransformations are then simultaneously used by the pathway design algorithm (novoStoic) to trace both metabolites and molecular moieties through balanced bio-conversion strategies. We demonstrate the use of novoStoic in bypassing steps in existing pathways through putative transformations, assembling complex pathways blending both known and putative steps toward pharmaceuticals, and postulating ways to biodegrade xenobiotics.
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Affiliation(s)
- Akhil Kumar
- The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
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12
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Ding S, Liao X, Tu W, Wu L, Tian Y, Sun Q, Chen J, Hu QN. EcoSynther: A Customized Platform To Explore the Biosynthetic Potential in E. coli. ACS Chem Biol 2017; 12:2823-2829. [PMID: 28952720 DOI: 10.1021/acschembio.7b00605] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Developing computational tools for a chassis-centered biosynthetic pathway design is very important for a productive heterologous biosynthesis system by considering enormous foreign biosynthetic reactions. For many cases, a pathway to produce a target molecule consists of both native and heterologous reactions when utilizing a microbial organism as the host organism. Due to tens of thousands of biosynthetic reactions existing in nature, it is not trivial to identify which could be served as heterologous ones to produce the target molecule in a specific organism. In the present work, we integrate more than 10,000 E. coli non-native reactions and utilize a probability-based algorithm to search pathways. Moreover, we built a user-friendly Web server named EcoSynther. It is able to explore the precursors and heterologous reactions needed to produce a target molecule in Escherichia coli K12 MG1655 and then applies flux balance analysis to calculate theoretical yields of each candidate pathway. Compared with other chassis-centered biosynthetic pathway design tools, EcoSynther has two unique features: (1) allow for automatic search without knowing a precursor in E. coli and (2) evaluate the candidate pathways under constraints from E. coli physiological states and growth conditions. EcoSynther is available at http://www.rxnfinder.org/ecosynther/ .
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Affiliation(s)
- Shaozhen Ding
- Shanghai Institutes
for Biological Sciences, Chinese Academy of Sciences, Shanghai 200333, People’s Republic of China
- Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
| | - Xiaoping Liao
- Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
| | - Weizhong Tu
- Wuhan LifeSynther
Science and Technology Co. Limited, Wuhan, 430070, People’s Republic of China
| | - Ling Wu
- Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
| | - Yu Tian
- Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
- University of
Chinese Academy of Sciences, Beijing 100864, People’s Republic of China
| | - Qiuping Sun
- Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
| | - Junni Chen
- Wuhan LifeSynther
Science and Technology Co. Limited, Wuhan, 430070, People’s Republic of China
| | - Qian-Nan Hu
- Shanghai Institutes
for Biological Sciences, Chinese Academy of Sciences, Shanghai 200333, People’s Republic of China
- Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
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13
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Wang L, Dash S, Ng CY, Maranas CD. A review of computational tools for design and reconstruction of metabolic pathways. Synth Syst Biotechnol 2017; 2:243-252. [PMID: 29552648 PMCID: PMC5851934 DOI: 10.1016/j.synbio.2017.11.002] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/06/2017] [Accepted: 11/06/2017] [Indexed: 11/28/2022] Open
Abstract
Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Satyakam Dash
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
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14
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Kim SM, Peña MI, Moll M, Bennett GN, Kavraki LE. A review of parameters and heuristics for guiding metabolic pathfinding. J Cheminform 2017; 9:51. [PMID: 29086092 PMCID: PMC5602787 DOI: 10.1186/s13321-017-0239-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 09/07/2017] [Indexed: 12/04/2022] Open
Abstract
Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.
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Affiliation(s)
- Sarah M Kim
- Department of Computer Science, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Matthew I Peña
- Department of BioSciences, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Mark Moll
- Department of Computer Science, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - George N Bennett
- Department of BioSciences, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, 6100 Main St., Houston, TX, 77005, USA.
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15
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Blaß LK, Weyler C, Heinzle E. Network design and analysis for multi-enzyme biocatalysis. BMC Bioinformatics 2017; 18:366. [PMID: 28797226 PMCID: PMC5553788 DOI: 10.1186/s12859-017-1773-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 07/30/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. RESULTS We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. CONCLUSION We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.
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Affiliation(s)
- Lisa Katharina Blaß
- Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany
| | - Christian Weyler
- Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany
| | - Elmar Heinzle
- Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany.
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16
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Sankar A, Ranu S, Raman K. Predicting novel metabolic pathways through subgraph mining. Bioinformatics 2017; 33:3955-3963. [DOI: 10.1093/bioinformatics/btx481] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/26/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aravind Sankar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Sayan Ranu
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Karthik Raman
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
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17
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Purdy HM, Reed JL. Evaluating the capabilities of microbial chemical production using genome-scale metabolic models. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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18
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Huang Y, Zhong C, Lin HX, Wang J. A Method for Finding Metabolic Pathways Using Atomic Group Tracking. PLoS One 2017; 12:e0168725. [PMID: 28068354 PMCID: PMC5221824 DOI: 10.1371/journal.pone.0168725] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 12/05/2016] [Indexed: 12/13/2022] Open
Abstract
A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.
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Affiliation(s)
- Yiran Huang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
- * E-mail: (YH); (CZ)
| | - Cheng Zhong
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
- * E-mail: (YH); (CZ)
| | - Hai Xiang Lin
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jianyi Wang
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning, China
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19
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Karim AS, Dudley QM, Jewett MC. Cell-Free Synthetic Systems for Metabolic Engineering and Biosynthetic Pathway Prototyping. Ind Biotechnol (New Rochelle N Y) 2016. [DOI: 10.1002/9783527807796.ch4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- Ashty S. Karim
- Northwestern University; Department of Chemical and Biological Engineering; 2145 Sheridan Road Evanston IL 60208 USA
- Northwestern University; Chemistry of Life Processes Institute; 2170 Campus Drive Evanston IL 60208 USA
| | - Quentin M. Dudley
- Northwestern University; Department of Chemical and Biological Engineering; 2145 Sheridan Road Evanston IL 60208 USA
- Northwestern University; Chemistry of Life Processes Institute; 2170 Campus Drive Evanston IL 60208 USA
| | - Michael C. Jewett
- Northwestern University; Department of Chemical and Biological Engineering; 2145 Sheridan Road Evanston IL 60208 USA
- Northwestern University; Chemistry of Life Processes Institute; 2170 Campus Drive Evanston IL 60208 USA
- Northwestern University; Robert H. Lurie Comprehensive Cancer Center; 676 North St. Clair Chicago IL 60611 USA
- Northwestern University; Simpson Querrey Institute for Bionanotechnology; 303 E. Superior Chicago IL 60611 USA
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20
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Carbonell P, Currin A, Jervis AJ, Rattray NJW, Swainston N, Yan C, Takano E, Breitling R. Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle. Nat Prod Rep 2016; 33:925-32. [PMID: 27185383 PMCID: PMC5063057 DOI: 10.1039/c6np00018e] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/11/2022]
Abstract
Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Andrew Currin
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Adrian J. Jervis
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Nicholas J. W. Rattray
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Neil Swainston
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Cunyu Yan
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Eriko Takano
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Rainer Breitling
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
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21
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Kuwahara H, Alazmi M, Cui X, Gao X. MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind. Nucleic Acids Res 2016; 44:W217-25. [PMID: 27131375 PMCID: PMC4987905 DOI: 10.1093/nar/gkw342] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 04/18/2016] [Indexed: 01/01/2023] Open
Abstract
To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. We developed a novel web server, called MRE, which, for a given pair of starting and desired compounds in a given chassis organism, ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. These unique, chassis-centered features distinguish MRE from existing pathway design tools and allow synthetic biologists to evaluate the design of their biosynthesis systems from a different angle. By using biosynthesis of a range of high-value natural products as a case study, we show that MRE is an effective tool to guide the design and optimization of heterologous biosynthesis pathways. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/.
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Affiliation(s)
- Hiroyuki Kuwahara
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
| | - Meshari Alazmi
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
| | - Xuefeng Cui
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
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22
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Johns NI, Blazejewski T, Gomes AL, Wang HH. Principles for designing synthetic microbial communities. Curr Opin Microbiol 2016; 31:146-153. [PMID: 27084981 DOI: 10.1016/j.mib.2016.03.010] [Citation(s) in RCA: 159] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 03/19/2016] [Accepted: 03/21/2016] [Indexed: 01/21/2023]
Abstract
Advances in synthetic biology to build microbes with defined and controllable properties are enabling new approaches to design and program multispecies communities. This emerging field of synthetic ecology will be important for many areas of biotechnology, bioenergy and bioremediation. This endeavor draws upon knowledge from synthetic biology, systems biology, microbial ecology and evolution. Fully realizing the potential of this discipline requires the development of new strategies to control the intercellular interactions, spatiotemporal coordination, robustness, stability and biocontainment of synthetic microbial communities. Here, we review recent experimental, analytical and computational advances to study and build multi-species microbial communities with defined functions and behavior for various applications. We also highlight outstanding challenges and future directions to advance this field.
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Affiliation(s)
- Nathan I Johns
- Department of Systems Biology, Columbia University Medical Center, New York, USA; Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Medical Center, New York, USA
| | - Tomasz Blazejewski
- Department of Systems Biology, Columbia University Medical Center, New York, USA; Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Medical Center, New York, USA
| | - Antonio Lc Gomes
- Department of Systems Biology, Columbia University Medical Center, New York, USA
| | - Harris H Wang
- Department of Systems Biology, Columbia University Medical Center, New York, USA; Department of Pathology and Cell Biology, Columbia University Medical Center, New York, USA.
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23
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Khater S, Anand S, Mohanty D. In silico methods for linking genes and secondary metabolites: The way forward. Synth Syst Biotechnol 2016; 1:80-88. [PMID: 29062931 PMCID: PMC5640692 DOI: 10.1016/j.synbio.2016.03.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 02/18/2016] [Accepted: 03/01/2016] [Indexed: 11/26/2022] Open
Abstract
In silico methods for linking genomic space to chemical space have played a crucial role in genomics driven discovery of new natural products as well as biosynthesis of altered natural products by engineering of biosynthetic pathways. Here we give an overview of available computational tools and then briefly describe a novel computational framework, namely retro-biosynthetic enumeration of biosynthetic reactions, which can add to the repertoire of computational tools available for connecting natural products to their biosynthetic gene clusters. Most of the currently available bioinformatics tools for analysis of secondary metabolite biosynthetic gene clusters utilize the “Genes to Metabolites” approach. In contrast to the “Genes to Metabolites” approach, the “Metabolites to Genes” or retro-biosynthetic approach would involve enumerating the various biochemical transformations or enzymatic reactions which would generate the given chemical moiety starting from a set of precursor molecules and identifying enzymatic domains which can potentially catalyze the enumerated biochemical transformations. In this article, we first give a brief overview of the presently available in silico tools and approaches for analysis of secondary metabolite biosynthetic pathways. We also discuss our preliminary work on development of algorithms for retro-biosynthetic enumeration of biochemical transformations to formulate a novel computational method for identifying genes associated with biosynthesis of a given polyketide or nonribosomal peptide.
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Affiliation(s)
- Shradha Khater
- Bioinformatics Center, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Swadha Anand
- Bioinformatics Center, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Debasisa Mohanty
- Bioinformatics Center, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
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24
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Designing overall stoichiometric conversions and intervening metabolic reactions. Sci Rep 2015; 5:16009. [PMID: 26530953 PMCID: PMC4632160 DOI: 10.1038/srep16009] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 10/07/2015] [Indexed: 02/07/2023] Open
Abstract
Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C2+ metabolites with higher carbon efficiency.
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25
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Ng CY, Khodayari A, Chowdhury A, Maranas CD. Advances in de novo strain design using integrated systems and synthetic biology tools. Curr Opin Chem Biol 2015; 28:105-14. [DOI: 10.1016/j.cbpa.2015.06.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 06/13/2015] [Accepted: 06/21/2015] [Indexed: 11/17/2022]
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26
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Ataman M, Hatzimanikatis V. Heading in the right direction: thermodynamics-based network analysis and pathway engineering. Curr Opin Biotechnol 2015; 36:176-82. [PMID: 26360871 DOI: 10.1016/j.copbio.2015.08.021] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 08/11/2015] [Accepted: 08/18/2015] [Indexed: 11/28/2022]
Abstract
Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods.
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Affiliation(s)
- Meric Ataman
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland.
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27
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Winkler JD, Erickson K, Choudhury A, Halweg-Edwards AL, Gill RT. Complex systems in metabolic engineering. Curr Opin Biotechnol 2015; 36:107-14. [PMID: 26319897 DOI: 10.1016/j.copbio.2015.08.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Revised: 08/05/2015] [Accepted: 08/06/2015] [Indexed: 01/11/2023]
Abstract
Metabolic engineers manipulate intricate biological networks to build efficient biological machines. The inherent complexity of this task, derived from the extensive and often unknown interconnectivity between and within these networks, often prevents researchers from achieving desired performance. Other fields have developed methods to tackle the issue of complexity for their unique subset of engineering problems, but to date, there has not been extensive and comprehensive examination of how metabolic engineers use existing tools to ameliorate this effect on their own research projects. In this review, we examine how complexity affects engineering at the protein, pathway, and genome levels within an organism, and the tools for handling these issues to achieve high-performing strain designs. Quantitative complexity metrics and their applications to metabolic engineering versus traditional engineering fields are also discussed. We conclude by predicting how metabolic engineering practices may advance in light of an explicit consideration of design complexity.
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Affiliation(s)
- James D Winkler
- Department of Chemical and Biological Engineering, University of Colorado-Boulder, Jennie Smoly Caruthers Biotechnology Building, Research Park, Boulder, CO 80303, USA
| | - Keesha Erickson
- Department of Chemical and Biological Engineering, University of Colorado-Boulder, Jennie Smoly Caruthers Biotechnology Building, Research Park, Boulder, CO 80303, USA
| | - Alaksh Choudhury
- Department of Chemical and Biological Engineering, University of Colorado-Boulder, Jennie Smoly Caruthers Biotechnology Building, Research Park, Boulder, CO 80303, USA
| | - Andrea L Halweg-Edwards
- Department of Chemical and Biological Engineering, University of Colorado-Boulder, Jennie Smoly Caruthers Biotechnology Building, Research Park, Boulder, CO 80303, USA
| | - Ryan T Gill
- Department of Chemical and Biological Engineering, University of Colorado-Boulder, Jennie Smoly Caruthers Biotechnology Building, Research Park, Boulder, CO 80303, USA.
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28
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Li M, Borodina I. Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae. FEMS Yeast Res 2015; 15:1-12. [PMID: 25238571 DOI: 10.1111/1567-1364.12213] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 05/13/2014] [Accepted: 09/15/2014] [Indexed: 11/29/2022] Open
Abstract
Synthetic biology and metabolic engineering enable generation of novel cell factories that efficiently convert renewable feedstocks into biofuels, bulk, and fine chemicals, thus creating the basis for biosustainable economy independent on fossil resources. While over a hundred proof-of-concept chemicals have been made in yeast, only a very small fraction of those has reached commercial-scale production so far. The limiting factor is the high research cost associated with the development of a robust cell factory that can produce the desired chemical at high titer, rate, and yield. Synthetic biology has the potential to bring down this cost by improving our ability to predictably engineer biological systems. This review highlights synthetic biology applications for design, assembly, and optimization of non-native biochemical pathways in baker's yeast Saccharomyces cerevisiae We describe computational tools for the prediction of biochemical pathways, molecular biology methods for assembly of DNA parts into pathways, and for introducing the pathways into the host, and finally approaches for optimizing performance of the introduced pathways.
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Affiliation(s)
- Mingji Li
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
| | - Irina Borodina
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
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29
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Dudley QM, Karim AS, Jewett MC. Cell-free metabolic engineering: biomanufacturing beyond the cell. Biotechnol J 2015; 10:69-82. [PMID: 25319678 PMCID: PMC4314355 DOI: 10.1002/biot.201400330] [Citation(s) in RCA: 217] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Revised: 07/24/2014] [Accepted: 08/22/2014] [Indexed: 12/20/2022]
Abstract
Industrial biotechnology and microbial metabolic engineering are poised to help meet the growing demand for sustainable, low-cost commodity chemicals and natural products, yet the fraction of biochemicals amenable to commercial production remains limited. Common problems afflicting the current state-of-the-art include low volumetric productivities, build-up of toxic intermediates or products, and byproduct losses via competing pathways. To overcome these limitations, cell-free metabolic engineering (CFME) is expanding the scope of the traditional bioengineering model by using in vitro ensembles of catalytic proteins prepared from purified enzymes or crude lysates of cells for the production of target products. In recent years, the unprecedented level of control and freedom of design, relative to in vivo systems, has inspired the development of engineering foundations for cell-free systems. These efforts have led to activation of long enzymatic pathways (>8 enzymes), near theoretical conversion yields, productivities greater than 100 mg L(-1) h(-1) , reaction scales of >100 L, and new directions in protein purification, spatial organization, and enzyme stability. In the coming years, CFME will offer exciting opportunities to: (i) debug and optimize biosynthetic pathways; (ii) carry out design-build-test iterations without re-engineering organisms; and (iii) perform molecular transformations when bioconversion yields, productivities, or cellular toxicity limit commercial feasibility.
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Affiliation(s)
| | | | - Michael C. Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, USA
- Member, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA
- Member, Institute for Bionanotechnology in Medicine, Northwestern University, Chicago, IL, USA
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30
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Ding Y, Wu F, Tan C. Synthetic Biology: A Bridge between Artificial and Natural Cells. Life (Basel) 2014; 4:1092-116. [PMID: 25532531 PMCID: PMC4284483 DOI: 10.3390/life4041092] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/02/2014] [Accepted: 12/11/2014] [Indexed: 12/24/2022] Open
Abstract
Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications.
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Affiliation(s)
- Yunfeng Ding
- Department of Biomedical Engineering, University of California Davis, One Shields Ave., Davis, CA 95616-5270, USA.
| | - Fan Wu
- Department of Biomedical Engineering, University of California Davis, One Shields Ave., Davis, CA 95616-5270, USA.
| | - Cheemeng Tan
- Department of Biomedical Engineering, University of California Davis, One Shields Ave., Davis, CA 95616-5270, USA.
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31
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Lewis DD, Villarreal FD, Wu F, Tan C. Synthetic biology outside the cell: linking computational tools to cell-free systems. Front Bioeng Biotechnol 2014; 2:66. [PMID: 25538941 PMCID: PMC4260521 DOI: 10.3389/fbioe.2014.00066] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 11/23/2014] [Indexed: 12/22/2022] Open
Abstract
As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.
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Affiliation(s)
- Daniel D. Lewis
- Integrative Genetics and Genomics, University of California Davis, Davis, CA, USA
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA
| | | | - Fan Wu
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA
| | - Cheemeng Tan
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA
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Araki M, Cox RS, Makiguchi H, Ogawa T, Taniguchi T, Miyaoku K, Nakatsui M, Hara KY, Kondo A. M-path: a compass for navigating potential metabolic pathways. Bioinformatics 2014; 31:905-11. [DOI: 10.1093/bioinformatics/btu750] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Morgat A, Axelsen KB, Lombardot T, Alcántara R, Aimo L, Zerara M, Niknejad A, Belda E, Hyka-Nouspikel N, Coudert E, Redaschi N, Bougueleret L, Steinbeck C, Xenarios I, Bridge A. Updates in Rhea--a manually curated resource of biochemical reactions. Nucleic Acids Res 2014; 43:D459-64. [PMID: 25332395 PMCID: PMC4384025 DOI: 10.1093/nar/gku961] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Rhea (http://www.ebi.ac.uk/rhea) is a comprehensive and non-redundant resource of expert-curated biochemical reactions described using species from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Rhea has been designed for the functional annotation of enzymes and the description of genome-scale metabolic networks, providing stoichiometrically balanced enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list and additional reactions), transport reactions and spontaneously occurring reactions. Rhea reactions are extensively curated with links to source literature and are mapped to other publicly available enzyme and pathway databases such as Reactome, BioCyc, KEGG and UniPathway, through manual curation and computational methods. Here we describe developments in Rhea since our last report in the 2012 database issue of Nucleic Acids Research. These include significant growth in the number of Rhea reactions and the inclusion of reactions involving complex macromolecules such as proteins, nucleic acids and other polymers that lie outside the scope of ChEBI. Together these developments will significantly increase the utility of Rhea as a tool for the description, analysis and reconciliation of genome-scale metabolic models.
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Affiliation(s)
- Anne Morgat
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland Genoscope-LABGeM, CEA, Evry, F-91057, France
| | - Kristian B Axelsen
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Thierry Lombardot
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Rafael Alcántara
- Equipe BAMBOO, INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin, F-38330, France
| | - Lucila Aimo
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Mohamed Zerara
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Anne Niknejad
- Cheminformatics and Metabolism Team, European Bioinformatics Institute, Hinxton, CB10 1SD, UK
| | - Eugeni Belda
- Department of Biochemistry, University of Geneva, Geneva, CH-1206, Switzerland
| | - Nevila Hyka-Nouspikel
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Elisabeth Coudert
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Nicole Redaschi
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Lydie Bougueleret
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
| | - Christoph Steinbeck
- Equipe BAMBOO, INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin, F-38330, France
| | - Ioannis Xenarios
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland Cheminformatics and Metabolism Team, European Bioinformatics Institute, Hinxton, CB10 1SD, UK
| | - Alan Bridge
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, CH-1206, Switzerland
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A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories. Comput Struct Biotechnol J 2014; 11:91-9. [PMID: 25379147 PMCID: PMC4212277 DOI: 10.1016/j.csbj.2014.08.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.
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Carbonell P, Parutto P, Herisson J, Pandit SB, Faulon JL. XTMS: pathway design in an eXTended metabolic space. Nucleic Acids Res 2014; 42:W389-94. [PMID: 24792156 PMCID: PMC4086079 DOI: 10.1093/nar/gku362] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at http://xtms.issb.genopole.fr, which provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework. The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.
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Affiliation(s)
- Pablo Carbonell
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | - Pierre Parutto
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | - Joan Herisson
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | | | - Jean-Loup Faulon
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
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Arora PK, Bae H. Integration of bioinformatics to biodegradation. Biol Proced Online 2014; 16:8. [PMID: 24808763 PMCID: PMC4012781 DOI: 10.1186/1480-9222-16-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 04/19/2014] [Indexed: 12/22/2022] Open
Abstract
Bioinformatics and biodegradation are two primary scientific fields in applied microbiology and biotechnology. The present review describes development of various bioinformatics tools that may be applied in the field of biodegradation. Several databases, including the University of Minnesota Biocatalysis/Biodegradation database (UM-BBD), a database of biodegradative oxygenases (OxDBase), Biodegradation Network-Molecular Biology Database (Bionemo) MetaCyc, and BioCyc have been developed to enable access to information related to biochemistry and genetics of microbial degradation. In addition, several bioinformatics tools for predicting toxicity and biodegradation of chemicals have been developed. Furthermore, the whole genomes of several potential degrading bacteria have been sequenced and annotated using bioinformatics tools.
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Affiliation(s)
- Pankaj Kumar Arora
- School of Biotechnology, Yeungnam University, Gyeongsan 712-749, Republic of Korea
| | - Hanhong Bae
- School of Biotechnology, Yeungnam University, Gyeongsan 712-749, Republic of Korea
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Fernández-Castané A, Fehér T, Carbonell P, Pauthenier C, Faulon JL. Computer-aided design for metabolic engineering. J Biotechnol 2014; 192 Pt B:302-13. [PMID: 24704607 DOI: 10.1016/j.jbiotec.2014.03.029] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 03/18/2014] [Accepted: 03/24/2014] [Indexed: 12/20/2022]
Abstract
The development and application of biotechnology-based strategies has had a great socio-economical impact and is likely to play a crucial role in the foundation of more sustainable and efficient industrial processes. Within biotechnology, metabolic engineering aims at the directed improvement of cellular properties, often with the goal of synthesizing a target chemical compound. The use of computer-aided design (CAD) tools, along with the continuously emerging advanced genetic engineering techniques have allowed metabolic engineering to broaden and streamline the process of heterologous compound-production. In this work, we review the CAD tools available for metabolic engineering with an emphasis, on retrosynthesis methodologies. Recent advances in genetic engineering strategies for pathway implementation and optimization are also reviewed as well as a range of bionalytical tools to validate in silico predictions. A case study applying retrosynthesis is presented as an experimental verification of the output from Retropath, the first complete automated computational pipeline applicable to metabolic engineering. Applying this CAD pipeline, together with genetic reassembly and optimization of culture conditions led to improved production of the plant flavonoid pinocembrin. Coupling CAD tools with advanced genetic engineering strategies and bioprocess optimization is crucial for enhanced product yields and will be of great value for the development of non-natural products through sustainable biotechnological processes.
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Affiliation(s)
- Alfred Fernández-Castané
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Tamás Fehér
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Pablo Carbonell
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Cyrille Pauthenier
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Jean-Loup Faulon
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
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